Memory management is vital for LLM agents in long-term and personalized interactions.Most previous work studies how to retrieve and use memory, but pays less attention to how memory is extracted.We find two main limitations in existing methods.First, extraction is “ahead-of-time”: the agent saves information before it knows future tasks.A single summary prompt often mixes details, events, and relations, so useful information is lost.Second, extraction is usually one-off. Without verification, errors and hallucinations may stay in memory for a long time. To address these limitations, we propose ProMem, a proactive memory extraction framework.It separates details, events, and relations, and uses different extraction strategies for each type.It also checks completeness to recover missed events and verifies facts at the atomic level to reduce hallucinations.Experiments show that ProMem improves memory completeness and QA accuracy, while keeping a good balance between quality and token cost.

In a rapidly evolving world where information updates swiftly, knowledge in large language models (LLMs) becomes outdated quickly. Retraining LLMs is not a cost-effective option, making knowledge editing (KE) without modifying parameters particularly necessary. We find that although existing retrieval-augmented generation (RAG)-based KE methods excel at editing simple knowledge, they struggle with KE in multi-hop question answering due to the issue of ”edit skipping”, which refers to skipping the relevant edited fact in inference. In addition to the diversity of natural language expressions of knowledge, edit skipping also arises from the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. To address this issue, we propose a novel Iterative Retrieval-Augmented Knowledge Editing method with guided decomposition (IRAKE) through the guidance from single edited facts and entire edited cases. Experimental results demonstrate that IRAKE mitigates the failure of editing caused by edit skipping and outperforms state-of-the-art methods for KE in multi-hop question answering.